Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install jeremylongshore-claude-code-plugins-plus-skills-plugins-crypto-trading-strategy-backtester-skills-backtesting-trgit clone https://github.com/jeremylongshore/claude-code-plugins-plus-skills.gitcp claude-code-plugins-plus-skills/SKILL.MD ~/.claude/skills/jeremylongshore-claude-code-plugins-plus-skills-plugins-crypto-trading-strategy-backtester-skills-backtesting-tr/SKILL.md---
name: backtesting-trading-strategies
description: 'Backtest crypto and traditional trading strategies against historical
data.
Calculates performance metrics (Sharpe, Sortino, max drawdown), generates equity
curves,
and optimizes strategy parameters. Use when user wants to test a trading strategy,
validate signals, or compare approaches.
Trigger with phrases like "backtest strategy", "test trading strategy", "historical
performance",
"simulate trades", "optimize parameters", or "validate signals".
'
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(python:*)
version: 2.0.0
author: Jeremy Longshore <jeremy@intentsolutions.io>
license: MIT
tags:
- crypto
- testing
- performance
compatibility: Designed for Claude Code, also compatible with Codex and OpenClaw
---
# Backtesting Trading Strategies
## Overview
Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in strategies, comprehensive performance metrics, and parameter optimization.
**Key Features:**
- 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
- Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)
- Parameter grid search optimization
- Equity curve visualization
- Trade-by-trade analysis
## Prerequisites
Install required dependencies:
```bash
set -euo pipefail
pip install pandas numpy yfinance matplotlib
```
Optional for advanced features:
```bash
set -euo pipefail
pip install ta-lib scipy scikit-learn
```
## Instructions
1. Fetch historical data (cached to `${CLAUDE_SKILL_DIR}/data/` for reuse):
```bash
python ${CLAUDE_SKILL_DIR}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d
```
2. Run a backtest with default or custom parameters:
```bash
python ${CLAUDE_SKILL_DIR}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y
python ${CLAUDE_SKILL_DIR}/scripts/backtest.py \
--strategy rsi_reversal \
--symbol ETH-USD \
--period 1y \
--capital 10000 \ # 10000: 10 seconds in ms
--params '{"period": 14, "overbought": 70, "oversold": 30}'
```
3. Analyze results saved to `${CLAUDE_SKILL_DIR}/reports/` -- includes `*_summary.txt` (performance metrics), `*_trades.csv` (trade log), `*_equity.csv` (equity curve data), and `*_chart.png` (visual equity curve).
4. Optimize parameters via grid search to find the best combination:
```bash
python ${CLAUDE_SKILL_DIR}/scripts/optimize.py \
--strategy sma_crossover \
--symbol BTC-USD \
--period 1y \
--param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}' # HTTP 200 OK
```
## Output
### Performance Metrics
| Metric | Description |
|--------|-------------|
| Total Return | Overall percentage gain/loss |
| CAGR | Compound annual growth rate |
| Sharpe Ratio | Risk-adjusted return (target: >1.5) |
| Sortino Ratio | Downside risk-adjusted return |
| Calmar Ratio | Return divided by max drawdown |
### Risk Metrics
| Metric | Description |
|--------|-------------|
| Max Drawdown | Largest peak-to-trough decline |
| VaR (95%) | Value at Risk at 95% confidence |
| CVaR (95%) | Expected loss beyond VaR |
| Volatility | Annualized standard deviation |
### Trade Statistics
| Metric | Description |
|--------|-------------|
| Total Trades | Number of round-trip trades |
| Win Rate | Percentage of profitable trades |
| Profit Factor | Gross profit divided by gross loss |
| Expectancy | Expected value per trade |
### Example Output
```
================================================================================
BACKTEST RESULTS: SMA CROSSOVER
BTC-USD | [start_date] to [end_date]
================================================================================
PERFORMANCE | RISK
Total Return: +47.32% | Max Drawdown: -18.45%
CAGR: +47.32% | VaR (95%): -2.34%
Sharpe Ratio: 1.87 | Volatility: 42.1%
Sortino Ratio: 2.41 | Ulcer Index: 8.2
--------------------------------------------------------------------------------
TRADE STATISTICS
Total Trades: 24 | Profit Factor: 2.34
Win Rate: 58.3% | Expectancy: $197.17
Avg Win: $892.45 | Max Consec. Losses: 3
================================================================================
```
## Supported Strategies
| Strategy | Description | Key Parameters |
|----------|-------------|----------------|
| `sma_crossover` | Simple moving average crossover | `fast_period`, `slow_period` |
| `ema_crossover` | Exponential MA crossover | `fast_period`, `slow_period` |
| `rsi_reversal` | RSI overbought/oversold | `period`, `overbought`, `oversold` |
| `macd` | MACD signal line crossover | `fast`, `slow`, `signal` |
| `bollinger_bands` | Mean reversion on bands | `period`, `std_dev` |
| `breakout` | Price breakout from range | `lookback`, `threshold` |
| `mean_reversion` | Return to moving average | `period`, `z_threshold` |
| `momentum` | Rate of change momentum | `period`, `threshold` |
## Configuration
Create `${CLAUDE_SKILL_DIR}/config/settings.yaml`:
```yaml
data:
provider: yfinance
cache_dir: ./data
backtest:
default_capital: 10000 # 10000: 10 seconds in ms
commission: 0.001 # 0.1% per trade
slippage: 0.0005 # 0.05% slippage
risk:
max_position_size: 0.95
stop_loss: null # Optional fixed stop loss
take_profit: null # Optional fixed take profit
```
## Error Handling
See `${CLAUDE_SKILL_DIR}/references/errors.md` for common issues and solutions.
## Examples
See `${CLAUDE_SKILL_DIR}/references/examples.md` for detailed usage examples including:
- Multi-asset comparison
- Walk-forward analysis
- Parameter optimization workflows
## Files
| File | Purpose |
|------|---------|
| `scripts/backtest.py` | Main backtesting engine |
| `scripts/fetch_data.py` | Historical data fetcher |
| `scripts/strategies.py` | Strategy definitions |
| `scripts/metrics.py` | Performance calculations |
| `scripts/optimize.py` | Parameter optimization |
## Resources
- [yfinance](https://github.com/ranaroussi/yfinance) - Yahoo Finance data
- [TA-Lib](https://ta-lib.org/) - Technical analysis library
- [QuantStats](https://github.com/ranaroussi/quantstats) - Portfolio analytics